Prior art approaches exist to convert text into corresponding sounds. Such techniques permit, for example, the conversion of text into audible synthesized speech. Many such approaches use phonemes that are units of a phonetic system of the relevant spoken language and that are usually perceived to be single distinct sounds in the spoken language. Using phonemes in this way in fact constitutes a relatively effective and accurate mechanism to achieve telling results. Unfortunately, however, prior art techniques do not always reliably select the correct phonemes.
Part of the problem stems from the fact that, in many spoken languages that have a corresponding symbolic alphabet, one or more of the symbols have more than one proper pronunciation. As a result, some symbols have more than one potentially appropriate phoneme (or set of phonemes) associated therewith. Various prior art approaches have been suggested to attempt mitigating the effect of this circumstance. Unfortunately, these solutions generally tend to be computationally intensive and/or require a considerable amount of memory. This tends to render such solutions inappropriate for use in resource-limited platforms (such as, for example, cellular telephones) where computational capacity itself and/or electric power can be considerably constrained.
For example, one prior art approach (known in at least some circles as “N-gram analysis”) uses a combination of probability analysis and grammatical context to weight a corresponding conclusion regarding pronunciation of a given word. To illustrate, the word “read” can be enunciated in English in either of two ways depending upon the grammatical context. By storing the rules regarding such context and by examining other words around the word “read” in view of those rules, one can potentially deduce a correct pronunciation for a given instance of the word. Again, however, such an approach often requires at least a significant quantity of memory as well as a fairly elaborate development and manipulation of contextual rules.
Many prior art approaches also fall short in view of another common occurrence; the need to pronounce a proper name or other word that is not in the dictionary of the process. To ameliorate, at least to some extent, this problem, the prior art suggests permitting a user to train the process by introducing the word along with its pronunciation. This approach, however, can be time consuming, tedious, confusing to the user, and again highly consumptive of memory and computational capacity.
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